/*
 * Artificial Intelligence for Humans
 * Volume 2: Nature Inspired Algorithms
 * Java Version
 * http://www.aifh.org
 * http://www.jeffheaton.com
 *
 * Code repository:
 * https://github.com/jeffheaton/aifh
 *
 * Copyright 2014 by Jeff Heaton
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * For more information on Heaton Research copyrights, licenses
 * and trademarks visit:
 * http://www.heatonresearch.com/copyright
 */
package com.heatonresearch.aifh.examples.ga.tsp

import com.heatonresearch.aifh.genetic.genome.IntegerArrayGenome
import com.heatonresearch.aifh.learning.MLMethod
import com.heatonresearch.aifh.learning.score.ScoreFunction

/**
 * Calculate a score for the TSP.
 * @param cities The path of cities to visit.
 */
class TSPScore(val cities: Array[City]) extends ScoreFunction {

  override def calculateScore(phenotype: MLMethod): Double = {
    var result = 0.0
    val genome = phenotype.asInstanceOf[IntegerArrayGenome]
    val path = genome.getData
    for(i <- 0 until cities.length - 1) {
      val city1 = cities(path(i))
      val city2 = cities(path(i + 1))
      val dist = city1.proximity(city2)
      result += dist
    }
    result
  }

  override def shouldMinimize: Boolean = true
}